Cancer
Identification of a MicroRNA
Panel for Clear-cell Kidney Cancer
David Juan, Gabriela Alexe, Travis Antes, Huiqing Liu, Anant Madabhushi, Charles Delisi,
Shridhar Ganesan, Gyan Bhanot, and Louis S. Liou
OBJECTIVES
METHODS
RESULTS
CONCLUSIONS
To identify a robust panel of microRNA signatures that can classify tumor from normal kidney
using microRNA expression levels. Mounting evidence suggests that microRNAs are key players
in essential cellular processes and that their expression pattern can serve as diagnostic biomarkers
for cancerous tissues.
We selected 28 clear-cell type human renal cell carcinoma (ccRCC), samples from patientmatched specimens to perform high-throughput, quantitative real-time polymerase chain reaction analysis of microRNA expression levels. The data were subjected to rigorous statistical
analyses and hierarchical clustering to produce a discrete set of microRNAs that can robustly
distinguish ccRCC from their patient-matched normal kidney tissue samples with high
confidence.
Thirty-five microRNAs were found that can robustly distinguish ccRCC from their patientmatched normal kidney tissue samples with high confidence. Among this set of 35 signature
microRNAs, 26 were found to be consistently downregulated and 9 consistently upregulated in
ccRCC relative to normal kidney samples. Two microRNAs, namely, MiR-155 and miR-21,
commonly found to be upregulated in other cancers, and miR-210, induced by hypoxia, were also
identified as overexpressed in ccRCC in our study. MicroRNAs identified as downregulated in
our study can be correlated to common chromosome deletions in ccRCC.
Our analysis is a comprehensive, statistically relevant study that identifies the microRNAs dysregulated in ccRCC, which can serve as the basis of molecular markers for diagnosis. UROLOGY 75:
835– 841, 2010. © 2010 Elsevier Inc.
R
enal cell carcinoma (RCC) is the most common
neoplasm in the adult kidney, accounting for 3%
of all malignancies in the United States. This
year, more than 50 000 men and women will be diagnosed with RCC and about 12 000 people will die because of this disease.1 In recent years, studies on the
biological mechanisms that contribute to clear-cell RCC
(ccRCC) have focused on mutations to the genome, expression of protein coding genes, and epigenetic changes.
Increasing evidence indicates, however, that dysregulation
of a class of noncoding RNA genes, microRNAs, is also
David Juan and Gabriela Alexe are joint first authors.
Gyan Bhanot and Louis S. Liou are joint corresponding authors.
From the Department of Pathology, Boston University, Boston, Massachusetts; The
Broad Institute of MIT and Harvard, Cambridge, Massachusetts; System Biosciences,
Mountain View, California; BioMaPS Institute, Rutgers University, Piscataway, New
Jersey; Departments of Molecular Biology and Biochemistry, and Physics, Rutgers
University, Piscataway, New Jersey; Department of Biomedical Engineering, Boston
University, Boston, Massachusetts; the Cancer Institute of New Jersey, New Brunswick, New Jersey; The Simons Center for Systems Biology, Institute for Advanced
Study, Princeton, New Jersey; and Cambridge Health Alliance, Harvard Medical
School, Cambridge, Massachusetts
Reprint requests: Louis S. Liou, M.D., Ph.D., Department of Pathology, Boston
University, 670 Albany Street Room 441, Boston, MA 02118. E-mail: Louis.
liou@bmc.org
Submitted: September 30, 2008, accepted (with revisions): October 19, 2009
© 2010 Elsevier Inc.
All Rights Reserved
associated with cancer and their expression profiles can
be correlated with diseases pathogenesis and diagnosis.2
Dysregulation of microRNAs has been observed in many
cancers, including solid tumors and hematological malignancies.3
MicroRNAs are a class of naturally occurring, noncoding RNAs that regulate protein expression by targeting
the messenger RNA of protein coding genes for either
translation repression or transcriptional modulation.3
They are a class of gene regulators that are endogenously
produced to play important roles in a wide range of
biological functions, including cellular differentiation,
development, and apoptosis.4
Connections between microRNA expression and cancer have been made on several levels. Altered patterns of
microRNA expression have been shown to be associated
with a variety of tumors.2 Also, microRNAs have been
implicated to function as either putative tumor suppressors or oncogenes, and multiple microRNAs are located
within cancer-associated chromosomal fragile sites, which
are susceptible to point mutation, amplification, deletion,
or translocation.5 Indeed, with microRNAs estimated to
regulate 30% of all gene transcripts, it is quite possible
that their aberrant expression might contribute to
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doi:10.1016/j.urology.2009.10.033
835
ccRCC formation by altering the balance in favor of
oncogenes while inhibiting tumor suppressor genes.6
In this report, we used a real-time quantitative polymerase chain reaction (qPCR) procedure designed to
identify the global expression patterns of microRNAs in
ccRCC and their patient-matched normal kidney tissues.
Our goal was to identify a robust panel of microRNA
signatures that can classify normal kidney from ccRCC
using microRNA expression levels. To accomplish this,
we first applied a stringent analysis to find a suitable
endogenous control, and then applied principal component analysis and consensus clustering analysis to identify
a panel of 35 microRNAs out of 384 profiled that can
accurately (with high specificity and sensitivity) and robustly (independent of data perturbation and method
of clustering) distinguish normal from ccRCC tissue
samples.
MATERIAL AND METHODS
Tissue Specimens
A total of 28 clear-cell RCC tissue specimens, along with their
patient-matched normal kidney tissue, were obtained from patients at Boston Medical Center and Cleveland Clinic immediately after radical nephrectomy. Each specimen was then
examined by a pathologist at the respective institution and
histologically classified. Twelve of the specimens were classified
as high-grade RCC (Fuhrman grade 3 or 4), whereas 16 of the
specimens were classified as low-grade RCC (Fuhrman grade 1
or 2). Institutional Review Board–approved informed consent
for the collection of specimens was obtained from all patients.
These specimens were then put into 5 volumes of RNA preservatives, set at 4°C for 1 hour, and stored at ⫺80°C until RNA
extraction.
RNA Extraction and cDNA Synthesis
Total RNA was extracted by homogenizing approximately 0.2 g
of frozen tissue with a Powergen 35 tissue homogenizer (Fisher
scientific, Pittsburgh, PA), followed by isolation using the mirVANA miRNA Isolation Kit (Ambion, Austin, TX). The
purified RNA was subsequently assessed for concentration using
a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE). Two micrograms of the purified RNA
were reverse-transcribed into first-strand cDNA using the
QuantiMir RT Kit (System Biosciences, Mountain View, CA),
which converts all small RNAs simultaneously into detectable
cDNAs for real-time PCR.
microRNA-specific forward primer was identical to the target
microRNA being measured. All reactions were subjected to
standard real-time PCR thermocycling conditions and performed in triplicate using an ABI 7900HT Sequence Detection
System with PowerSYBR reagents (Applied Biosystems, Foster
City, CA). Expression levels of the microRNAs were measured
using the comparative ct method and normalized to an endogenous control. Of the 384 microRNA measurements on the HT
384 microRNA profiler, 12 microRNAs were undetectable and
372 microRNAs provided reliable measurements across all the
patient RNA samples. The raw datasets produced on the cancer
microRNA qPCR 96-well array as well as the HT384 microRNA Profiler array are presented in Supplementary Tables
S1 and S2 (published online), respectively. Supplementary Tables S3 and S4 (published online) list the relative fold changes
in microRNA expression for each sample profiled on the cancer
microRNA qPCR 96-well array and the HT384 microRNA
Profiler array, respectively.
Endogenous MicroRNA Reference Control
For relative quantification, the normalization of each microRNA expression is defined relative to endogenous control
(housekeeping) microRNA with respect to each sample, by
dividing the raw microRNA intensity value to the value of the
endogenous control. Ten samples profiled on the 96-well cancer
microRNA qPCR Array were used to identify the endogenous
control. The variation in each microRNA for all tumor (t) and
normal/reference (n) samples was estimated using scores and
permutation P values for several tests: Mann–Whitney (Wilcoxon rank sum for paired T/N samples), Kolmogorov–Smirnov, F test, variance, and rank invariant test.7 These scores
were normalized and combined into an average global score,
with the lowest score determining the endogenous control.
Statistical Analysis
Data from the comparative ct method were evaluated using
the signal-to-noise ratio (SNR) test to identify the microRNAs
able to distinguish between tumor and normal phenotypes. The
P values for the SNR scores were determined using 1000 permutation tests with multiple hypothesis correction using the
False Discovery Rate (FDR) and q values.8 We then used
principal component analysis to further reduce the dimensionality of the data.
Furthermore, to evaluate the predictive power of the
microRNAs, several classification models, including Weighted
Voting (WV) and k-Nearest Neighbor (kNN), were applied to
the above identified markers to assess their predictive accuracy
based on the leave-one-out (LOO) cross validation test. These
tests were conducted using the class prediction module of BRB
Tools (http://linus.nci.nih.gov/BRB-ArrayTools.html).
Quantitative Real-time PCR
MicroRNA expression profiling was performed using real-time
PCR in either a 96-well using the cancer microRNA qPCR
Array (System Biosciences), 384-well format using the HT384
microRNA Profiler Array (System Biosciences), or in a Custom
60 microRNA array panel using a 384-well reaction plate. Each
real-time PCR reaction in the 96-well format contained 5.0 ng
of synthesized cDNA, 300 nM Universal reverse primer (System Biosciences), and 500 nM microRNA-specific forward assay primer in a total reaction volume of 20 ␮L. For the 384-well
real-time PCR reactions, the volume was performed at 5 ␮L
while keeping the reagent ratios the same. The sequence of the
836
RESULTS
We investigated the microRNA expression profile in
ccRCC by initially profiling 10 patient-matched samples
(patients 9-18) for 384 microRNAs using real-time PCR.
This initial survey detected 86 microRNAs with significant fold changes in expression level, of which 84 were
found to be downregulated (⫺1.2- to ⫺50-fold), whereas
2 microRNAs were found to be upregulated (miR592 ⫹
2.27, miR210 ⫹ 6.23-fold).
UROLOGY 75 (4), 2010
Table 1. A description of the samples used in the study and the platforms they were analyzed on
Number
Patient ID
Fuhrman
Grade
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
YR00-11
YR00-17
YR01-19
YR01-37
YR05-27
YR06-29
YR06-37
YR00-11
YR07-79
YR01-21
YR04-002
YR05-34
YR06-38
YR06-119
YR07-002
YR07-004
YR07-24
YR06-31
YR00-02
YR00-18
YR00-20
YR01-02
YR01-05
YR01-06
YR01-23
YR01-78
YR01-84
YR05-14
3
4
2
3
3
3
2
2
2
3
2
4
2
2
2
2
4
3
3
3
3
2
1
2
1
1
2
2
Array
Objective
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well cancer qPCR array
Endogenous control &
96-Well array & HT384 microRNA profiler Endogenous control &
96-Well array & HT384 microRNA profiler Endogenous control &
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
HT384 microRNA profiler
Profile establishment
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
Rencamir panel
Refinement of profile
profile
profile
profile
profile
profile
profile
profile
profile
profile
profile
establishment
establishment
establishment
establishment
establishment
establishment
establishment
establishment
establishment
establishment
The first set of 10 matching normal/tumor samples were analyzed for 96 microRNA expression levels to identify an endogenous control
and a preliminary discriminatory panel. Two of these sample pairs and a set of 8 new sample pairs were analyzed in a 384-well format
to validate and establish the panel. Finally, a new set of 10 samples pairs were analyzed on the “Rencamir” panel, which included the
best set of microRNA from first 2 experiments augmented with a set chosen from published data. The final step resulted in a list of 35
microRNAs that are our final optimal set of discriminatory microRNAs, which can distinguish between tumor and normal with perfect
(100%) accuracy in leave-one-out validation.
To further refine the microRNA test set, we investigated a select panel of 60 microRNAs. This panel consisted of microRNAs that we found dysregulated from the
HT384 microRNA Profiler array experiment, 96-well
cancer microRNA qPCR Array experiment, and a literature search. The 60 microRNA panel was then tested on
10 additional ccRCC patients (patients 19-28). What
emerged was a robust ccRCC microRNA signature panel
consisting of 35 microRNAs that can discriminate accurately between ccRCC and normal patient-matched tissue. Details of the patient samples used for each part of
the profiling are given in Table 1. We used distinct
patient cohorts for panel identification and refinement
(training and test samples).
Identification of the Endogenous Control
Statistical analysis of the dataset generated from the
96-well cancer microRNA qPCR Array identified mir106 b as having the least variation in expression between
ccRCC and patient-matched normal tissue across all 10
samples. The complete analysis, which describes why we
chose this endogenous control, is presented in Supplementary Table S5 (published online).
UROLOGY 75 (4), 2010
MicroRNA Expression Patterns
Stratify ccRCC From Normal Kidney
We initially profiled a set of 384 human microRNAs in
10 ccRCC and their paired normal kidney tissues to
identify differences in microRNA expression patterns.
The SNR test was applied to rank the microRNAs with
respect to the “Tumor vs Normal” class membership. Of
the 372 detectable microRNAs from the HT384 qPCR
array data, 86 microRNAs were found to be at a significance
level of P ⬍.01 and FDR ⬍0.05, with 2 microRNAs
consistently upregulated and 84 microRNAs downregulated in ccRCC. The entire dataset is provided in Supplementary Table S6 (published online).
We then used principal component analysis on the full
set of microRNA expression data and found that only a
few (⬃10) principal components were needed to separate
tumors from normal. By looking at the coefficients of the
eigenvectors of the principal components with largest
eigenvalues, we identified the microRNAs responsible for
85% of the variation in the data distinguishing tumors
from normal. These had excellent overlap with the microRNA identified by SNR above. Figure 1 shows the
projection of the tumor and normal samples on the first
2 principal components.
837
Figure 1. Projection of the Tumor and Normal log2 quantified and outlier corrected expression ratios on the first 2
principal components using Mir-106 b as baseline for 10
tumor and 10 normal samples in 3 replicates. These 2
principal components represent ⬃85% of the variation in
the data.
these 35 microRNAs is shown in Figure 3. Again, the
triplicate samples cluster together at the bottom of the
sample dendrogram showing minimal experimental noise.
We applied several classification methods to refine the
microRNA in our RencaMir panel using 10 tumor samples and 10 reference samples created on the Custom 60
microRNA qPCR array. LOO cross validation was performed with the classifiers constructed over a grid of
significance levels (t test) for microRNA selection based
on the most discriminative individual microRNA markers. At 0.5% significance level, we achieved 100% accuracy from Diagonal Linear Discriminant predictor and
1-Nearest Neighbor, Compound covariate Predictor,
3-Nearest Neighbor, and Nearest Centroid and Support
Vector Machine. The following 10 microRNAs were consistently selected by each of the 4 classifiers in all LOO
experiments: miR-200c, miR-185, miR-34a, miR-142-3p,
miR-21, miR-155, miR-224, miR-210, and miR-592.
COMMENT
Identification of a MicroRNA Signature in ccRCC
To identify a focused but robust panel of microRNAs to
distinguish normal kidney tissue from ccRCC, we selected 60 microRNAs for analysis. This collection was an
overlap of the top 36 microRNA markers identified in
the previous 384 microRNA dataset, 8 microRNAs identified in the 96 microRNA dataset, and 10 microRNAs
identified in the literature as potentially relevant to
ccRCC, but not identified by initial experimental analysis. We added the following set of “literature curated”
microRNAs, which were believed to target genes involved in kidney cancer: miR-195, miR-768-5p, miR-584,
miR-371, miR-212, miR-449, miR-421, miR-185, miR-34a,
and miR-34b (Supplementary Table S10 [published online]). The custom panel contained miR-199 (a ⫹ b)
twice as a check for noise level and also 2 additional
controls, snoRNA 43 (RNU43), and miR-106 b. Details
and references of the collection of microRNAs are presented in Supplementary Table S10 (published online).
The ability of this 60-microRNA panel to differentiate
ccRCC from normal kidney was then tested on additional samples of ccRCC and normal adjacent kidney
from 10 new patients (patient 19-28). The raw dataset
and the results after applying the comparative ct method
are listed in Supplementary Tables S7 and S8 (published
online), respectively.
Supervised analysis using the SNR test identified 35
microRNAs (26 downregulated and 9 upregulated microRNAs) as an optimal set to discriminate normal kidney from ccRCC. These are listed in Supplementary
Table S9 (published online) and correspond to a significance of P ⬍.05 and FDR ⬍0.05. We call this microRNA panel “RencaMir.” Three of the “literature curetted” microRNA (mir-34a, mir-34b, mir-185) made our
final panel. The fold change in these 35 microRNAs is
depicted in Figure 2 and a composite heat map of all the
measurements (normal ⫹ tumor) for the expression of
838
In this study, we set out to identify whether microRNA
expression signatures could be used to distinguish ccRCC
from normal kidney tissue in patient-matched samples.
Our first task established microRNA-106 b as the best
endogenous control and all subsequent measurements
were normalized to this reference. Although published
data on microRNA-106 b have identified it as overexpressed in several types of cancers and functionally linked
to proliferation and antiapoptotic pathways,9 there is also
evidence that this microRNA is invariant in many tissues.10 Previous studies have established the use of “oncogenic” microRNAs as endogenous controls including
let-7a and microRNA-16 in breast cancer.11 Therefore,
we are confident in our analysis that establishes microRNA-106 b as a suitable ccRCC endogenous control.
However, the possibility exists that the normal tissue
used in our paired normal-tumor sample set could harbor
alterations in the microRNA-106-b regulation as a first
step in the process of malignant transformation. A comparison between the tumor samples and normal tissue
from nonmalignant kidney specimens of nephrectomies
(nonmatched) could resolve this issue, although it is
beyond the scope of this article.
We then selected 10 patients with various grades of
ccRCC and profiled 384 microRNAs in triplicate using
qPCR for each tumor and normal RNA sample, for a
total 22 980 individual real-time qPCR data points. From
this initial study, we discovered a subset of microRNAs
that can robustly and accurately distinguish between
normal and tumor in ccRCC patients. We then independently tested the performance of this microRNA set on
samples from an additional 10 patients, for a total of 20
unique ccRCC patients, to better define a more discrete
set of differentially expressed microRNAs.
What emerged from our studies is a robust set of 35
microRNAs that will be used for future validation in
larger numbers of ccRCC patients. Within this set of 35
UROLOGY 75 (4), 2010
Figure 2. Graphical representation of the average fold change levels for the 9 upregulated and 26 downregulated microRNA
found across 10 ccRCC samples, with mir-106 b as the endogenous control.
Figure 3. Heat map of the 35 microRNAs that can be used to distinguish between ccRCC and patient-matched normal kidney
tissue. This panel of 35 microRNAs corresponds to 9 upregulated and 26 downregulated microRNAs found through SNR test using
microRNA 106 b as the endogenous control. Red signifies upregulation while green signifies downregulation. Note that replicate
samples cluster together, showing that measurement noise is well controlled (Color version available online).
ccRCC signature microRNAs, 26 were downregulated
and 9 were upregulated (Fig. 2). This is consistent with
other findings that numerous microRNAs in tumor samUROLOGY 75 (4), 2010
ples are expressed at lower levels than in normal tissues.2
To date, we know of one other study12 that demonstrated
differential microRNA expression patterns in RCC. In
839
this study, Gottardo et al12 reported 4 microRNAs (miR28, miR-185, miR-27, let-7f-2) significantly upregulated in
RCC. Our data show agreement with the increased expression of miR-185 in RCC. Here, we present a thorough
analysis using a larger patient-matched tissue sampling
population along with a more comprehensive microRNA
assay set measured, (384 in our study vs 161 human
microRNAs in their study), which reveal additional dysregulated microRNAs.
Four of the upregulated microRNAs identified in our
study (miR-21, miR-155, miR-34a, miR-210) have strong
correlations with other tumorigenic states and are commonly dysregulated in other solid tumors. For example,
the upregulation of miR-21 has been documented in at
least 6 solid tumors and linked to a variety of cancerrelated processes including proliferation, invasion, and
metastasis.13 MiR-155 is also overexpressed in cancers of
the breast, colon, lung, pancreas, and those of hematologic origin.13-15 Thus, our study provides another example of the apparent connection that these 2 commonly
upregulated microRNAs are associated with carcinogenesis in general. In addition to these 2 microRNAs, we
have identified an additional and unique set of microRNAs that are dysregulated in ccRCC malignancy, suggesting that this set of microRNAs could contribute to
the pathogenesis of ccRCC.
Two other upregulated microRNAs of great interest in
ccRCC identified in our study are miR-34a and miR-210.
Recently, overexpression of miR-34a was associated with
cell proliferation in an oxidative stress–induced rat renal
carcinogenesis model,16 while miR-210 was associated
with oxidative stress and hypoxia.17 Our study reveals
that miR-210 is overexpressed in ccRCC at 10-fold
higher levels compared with normal kidney. We believe
there could be a correlation between the overexpression
of miR-210 and common genomic alterations already
characterized in ccRCC. It has been well documented
that most ccRCC cases are associated with Von HippelLindau (VHL) gene deletions and mutations. VHL is a
known regulator of the transcription factor HIF-1A and
in cases where VHL is missing or mutated, elevated levels
of HIF-1A have been identified.18 Importantly, HIF-1A
has been demonstrated to induce the expression of miR210 through direct binding to the promoter region of
miR-210.17 Thus, our study supports the growing evidence that hypoxic conditions can induce miR-210
through HIF-1A–mediated transactivation and we provide a further connection to human ccRCC.
The overall decrease in microRNA expression is a
common theme in cancer. Sites of chromosomal deletions in tumors are often associated with the loss of tumor
suppressor genes,19 and key microRNAs downregulated
in our study map to known sites of chromosomal deletions associated with ccRCC. The most common deletion known to be involved in renal tumor development is
located at 3p21,20 where the RAS association Family 1
gene (RASSF1A) has been postulated to be the candi840
date tumor suppressor gene deleted and associated with
ccRCC.21 Also, located within this 3p21 locus is the
gene for miR-135a. A potential tumor suppressor role for
miR-135a may be in its putative regulatory binding site
within the 3=-untranslated region of HIF-1A.22 Our study
shows a consistent downregulation of miR-135a in
ccRCC, potentially alleviating the translational repression of and allowing for higher expression of HIF-1A.
Five additional microRNAs (miR-136, miR-154, miR337, miR-377, miR-411), also identified to be downregulated in our study, map to 14q32, which is another
common deletion site observed in ccRCC.23 Deletions
within this microRNA gene cluster region have also been
found in human epithelial ovarian cancer24 and bladder
cancer,25 suggesting a common loss of tumor suppressor
microRNA genes in at least 3 genitourinary organ tumors.
CONCLUSIONS
In summary, this study contributes to the growing understanding of the role that microRNAs play in cancer and
describes the global expression patterns of microRNAs in
patient-matched ccRCC and normal kidney tissues. Both
the microRNAs identified and the signaling pathways
they regulate may be important therapeutic targets in
human ccRCC. However, further studies are needed in
larger samples of ccRCC and also in other malignant and
benign kidney tumors.
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APPENDIX
SUPPLEMENTARY
DATA
Supplementary data associated with this article can be found,
in the online version, at doi:10.1016/j.urology.2009.10.033.
841